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Iguazio is a Tel Aviv, Israel-based data science company providing a multifunctional MLOps and machine learning automation-based solution for businesses in a variety of industries.


Iguazio develops and provides a data science platform built to enable the acceleration and scaling of development, deployment, and management of AI applications with MLOps and automation of machine learning pipelines. The company serves a variety of industries, including financial services, telecommunications, smart mobility, manufacturing, retail, ad-tech, gaming, healthcare, and energy and utilities.

MLOps pipeline automation

The platform enables the automation of machine learning pipelines, from data collection preparation and training to rapid deployment and ongoing monitoring in production. Unstructured and structured data can be ingested from any source in real time and unified. Subsequently, online and offline features can be built using Iguazio's Integrated Feature Store. Experimentation can be run over scalable serverless machine learning/data science runtimes with automated tracking, data versioning, and continuous integration/delivery support. Models and APIs can be deployed from a Jupyter notebook or an integrated development environment so production and model performance can be monitored.

Sharing and monitoring features

Iguazio defines features as properties that are used as inputs to a machine learning model. Feature engineering involves the generation of new features used for training (based on historical data) and for model prediction (based on online or real-time data). The Iguazio integrated feature store, which is a central component of Iguazio's MLOps platform, aims to solve problems related to the process of feature engineering via automation.

Iguazio feature store

According to Iguazio, its feature store is the first commercially available production-ready feature store that is part of an integrated and data science and engineering solution. The solution is designed to automate and simplify how features are engineered, with a single real-time and batch integration. In this process, high-level transformation logic is automatically converted to real-time serverless processing engines that can read from any online or offline source, handle any type of structures or unstructured data, and run computation graphs and native user code. Iguazio’s solution operates on a multi-model database, serving the computed features through a variety of APIs and formats, such as files, SQL queries, pandas, real-time REST APIs, time-series, and streaming.

Data transformation

The platform enables the creation of feature engineering processes with an integrated data transformation service, including feature aggregations with sliding windows, pre-built transformations, or custom logic in native Python code. Iguazio's goal is to provide a user-friendly API and SDK that data scientists can use to create features without requiring long data engineering cycles.

Feature catalog

Features can be shared, searched, collaborated on, and evaluated with detailed statistics and analysis on the Iguazio platform, enabling users to gain insight into how features correlate to data sources and models.

Integrated data and model monitoring

In addition, statistics can be shown in real time, enabling drift detection based on actual data drift. The Iguazio feature store is fully integrated with other features of the Data Science Platform, such as concept drift monitoring and feature monitoring.

Real-time feature engineering

Features are developed once for both offline and real-time deployment. Based on the information contained in incoming events or streams, the feature transformation pipeline processes features in real time and serves the results at millisecond level latency or forwards them directly into a stream.

Data governance and hybrid deployment

The platform saves the data history of features, with the tracking information capturing how each feature was generated for regulatory compliance purposes. Completed models can be deployed in a hybrid multi-cloud environment.


June 2021
Boston Limited and Iguazio partner to operationalize AI for enterprise applications.
February 2021
The AI Infrastructure Alliance launches with twenty-five members to create the canonical stack for artificial intelligence projects.
December 2020
Sheba Medical Center partners with Iguazio to develop real-time AI to optimize patient care.
November 2020
Iguazio’s data science platform achieves the AWS Outposts Ready designation.
July 2017
Iguazio raises a $33,000,000 series B round from CME Ventures, Dell Technologies Capital, Jerusalem Venture Partners, Magma Venture Partners, Pitango Venture Capital, Robert Bosch Venture Capital and Verizon Ventures.

Funding rounds



Ephrat Kirshen


Further reading


Documentaries, videos and podcasts





Ramesh Radhakrishnan
March 26, 2020
The new Dell EMC Reference Architecture for Iguazio speeds and simplifies the deployment of machine learning applications.


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